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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 1 of 33 A Refresher on Probability and Statistics Appendix C.

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Presentation on theme: "Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 1 of 33 A Refresher on Probability and Statistics Appendix C."— Presentation transcript:

1 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 1 of 33 A Refresher on Probability and Statistics Appendix C

2 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 2 of 33 What We’ll Do... Outline  Probability – basic ideas, terminology  Random variables,  Statistical inference – point estimation, confidence intervals, hypothesis testing

3 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 3 of 33 Terminology Statistic: Science of collecting, analyzing and interpreting data through the application of probability concepts. Probability: A measure that describes the chance (likelihood) that an event will occur. In simulation applications, probability and statistics are needed to  choose the input distributions of random variables,  generate random variables,  validate the simulation model,  analyze the output.

4 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 4 of 33 Event: Any possible outcome or any set of possible outcomes. Sample Space: Set of all possible outcomes. Ex: How is the weather today? What is the outcome when you toss a coin? Probability of an Event: Ex: Determine the probability of outcomes when an unfair coin is tossed? Toss the coin several times (say N) under the same conditions. Event A: Head appears Terminology Frequency of event A Relative frequency of event A Define Then

5 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 5 of 33 Probability Basics (cont’d.) Conditional probability  Knowing that an event F occurred might affect the probability that another event E also occurred  Reduce the effective sample space from S to F, then measure “size” of E relative to its overlap (if any) in F, rather than relative to S  Definition (assuming P(F)  0): E and F are independent if P(E  F) = P(E) P(F)  Implies P(E|F) = P(E) and P(F|E) = P(F), i.e., knowing that one event occurs tells you nothing about the other  If E and F are mutually exclusive, are they independent?

6 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 6 of 33 Random Variables One way of quantifying, simplifying events and probabilities A random variable (RV) is a number whose value is determined by the outcome of an experiment  Technically, a function or mapping from the sample space to the real numbers, but can usually define and work with a RV without going all the way back to the sample space  Think: RV is a number whose value we don’t know for sure but we’ll usually know something about what it can be or is likely to be  Usually denoted as capital letters: X, Y, W 1, W 2, etc. Probabilistic behavior described by distribution function

7 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 7 of 33 Random Variables Random Var: is a real and single valued function f(E): S R defined on each element E in the sample space S. event1 event2. eventn Thus for each event there is a corresponding random variable F( E ) R

8 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 8 of 33 Ex: In a simulation study how do we decide whether a customer is smoker or not? Let P( Smoker ) = 0.3 and P( Nonsmoker ) = 0.7 Generate a random number between [0,1] If it is < 0.3 smoker else nonsmoker Random Variables in Simulation

9 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 9 of 33 Discrete vs. Continuous RVs Two basic “flavors” of RVs, used to represent or model different things Discrete – can take on only certain separated values  Number of possible values could be finite or infinite Continuous – can take on any real value in some range  Number of possible values is always infinite  Range could be bounded on both sides, just one side, or neither

10 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 10 of 33 Discrete Random Variables Probability Mass Function is defined as 1/3 1/6 P(X) X Ex: Demand of a product, X has the following probability function X1X1 X4X4 X3X3 X2X2

11 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 11 of 33 Cumulative distribution function, F(x) is defined as where F(x) is the distribution or cumulative distribution function of X /6 1/2 1/6 F(x) x Ex: Discrete Random Variables

12 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 12 of 33 Expected Value of a Discrete R.V. Data set has a “center” – the average (mean) RVs have a “center” – expected value,  What expectation is not: The value of X you “expect” to get! E(X) might not even be among the possible values x 1, x 2, …  What expectation is: Repeat “the experiment” many times, observe many X 1, X 2, …, X n E(X) is what converges to (in a certain sense) as n  , where

13 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 13 of 33 Variances and Standard Deviation of a Discrete R.V. Data set has measures of “dispersion” –  Sample variance  Sample standard deviation RVs have corresponding measures  Weighted average of squared deviations of the possible values xi from the mean.  Standard deviation of X is

14 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 14 of 33 Continuous Distributions Now let X be a continuous RV  Possibly limited to a range bounded on left or right or both.  No matter how small the range, the number of possible values for X is always (uncountably) infinite  Not sensible to ask about P(X = x) even if x is in the possible range P(X = x) = 0  Instead, describe behavior of X in terms of its falling between two values!

15 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 15 of 33 Continuous Distributions (cont’d.) Probability density function (PDF) is a function f(x) with the following three properties: f(x)  0 for all real values x The total area under f(x) is 1: Although P(X=x)=0, Fun facts about PDFs  Observed X’s are denser in regions where f(x) is high  The height of a density, f(x), is not the probability of anything – it can even be > 1  With continuous RVs, you can be sloppy with weak vs. strong inequalities and endpoints

16 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 16 of 33 Cumulative distribution function Let I = [a,b] F(x) X 1 Continuous Random Variables

17 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 17 of 33 Ex: Lifetime of a laser ray device used to inspect cracks in aircraft wings is given by X, with pdf X (years) 1/2 2 3 Continuous Random Variables

18 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 18 of 33 Continuous Expected Values, Variances, and Standard Deviations Expectation or mean of X is  Roughly, a weighted “continuous” average of possible values for X  Same interpretation as in discrete case: average of a large number (infinite) of observations on the RV X Variance of X is Standard deviation of X is

19 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 19 of 33 Independent RVs X 1 and X 2 are independent if their joint CDF factors into the product of their marginal CDFs:  Equivalent to use PMF or PDF instead of CDF Properties of independent RVs:  They have nothing (linearly) to do with each other  Independence  uncorrelated – But not vice versa, unless the RVs have a joint normal distribution  Important in probability – factorization simplifies greatly  Tempting just to assume it whether justified or not Independence in simulation  Input: Usually assume separate inputs are indep. – valid?  Output: Standard statistics assumes indep. – valid?!?!?!?

20 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 20 of 33 Sampling Statistical analysis – estimate or infer something about a population or process based on only a sample from it  Think of a RV with a distribution governing the population  Random sample is a set of independent and identically distributed (IID) observations X 1, X 2, …, X n on this RV  In simulation, sampling is making some runs of the model and collecting the output data  Don’t know parameters of population (or distribution) and want to estimate them or infer something about them based on the sample

21 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 21 of 33 Sampling (cont’d.) Population parameter Population mean  = E(X) Population variance  2 Population proportion Parameter – need to know whole population Fixed (but unknown) Sample estimate Sample mean Sample variance Sample proportion Sample statistic – can be computed from a sample Varies from one sample to another – is a RV itself, and has a distribution, called the sampling distribution

22 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 22 of 33 Sampling Distributions Have a statistic, like sample mean or sample variance  Its value will vary from one sample to the next Some sampling-distribution results  Sample mean If Regardless of distribution of X,  Sample variance s 2 E(s 2 ) =  2  Sample proportion E( ) = p

23 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 23 of 33 Point Estimation A sample statistic that estimates (in some sense) a population parameter Properties  Unbiased: E(estimate) = parameter  Efficient: Var(estimate) is lowest among competing point estimators  Consistent: Var(estimate) decreases (usually to 0) as the sample size increases

24 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 24 of 33 Confidence Intervals A point estimator is just a single number, with some uncertainty or variability associated with it Confidence interval quantifies the likely imprecision in a point estimator  An interval that contains (covers) the unknown population parameter with specified (high) probability 1 –   Called a 100 (1 –  )% confidence interval for the parameter Confidence interval for the population mean  :

25 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 25 of 33 Confidence Intervals in Simulation Run simulations, get results View each replication of the simulation as a data point Random input  random output Form a confidence interval If you observe the system infinitely many times, 100 (1 –  )% of the time this inerval will contain the true population mean!

26 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 26 of 33 Hypothesis Tests Test some assertion about the population or its parameters Can never determine truth or falsity for sure – only get evidence that points one way or another Null hypothesis (H 0 ) – what is to be tested Alternate hypothesis (H 1 or H A ) – denial of H 0 H 0 :  = 6 vs. H 1 :   6 H 0 :  < 10 vs. H 1 :   10 H 0 :  1 =  2 vs. H 1 :  1   2 Develop a decision rule to decide on H 0 or H 1 based on sample data

27 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 27 of 33 Errors in Hypothesis Testing

28 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 28 of 33 p-Values for Hypothesis Tests Traditional method is “Accept” or Reject H 0 Alternate method – compute p-value of the test  p-value = probability of getting a test result more in favor of H 1 than what you got from your sample  Small p (like < 0.01) is convincing evidence against H 0  Large p (like > 0.20) indicates lack of evidence against H 0 Connection to traditional method  If p < , reject H 0  If p  , do not reject H 0 p-value quantifies confidence about the decision

29 Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 29 of 33 Hypothesis Testing in Simulation Input side  Specify input distributions to drive the simulation  Collect real-world data on corresponding processes  “Fit” a probability distribution to the observed real-world data  Test H 0 : the data are well represented by the fitted distribution Output side  Have two or more “competing” designs modeled  Test H 0 : all designs perform the same on output, or test H 0 : one design is better than another


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